Hamsa Bastani

About

Hamsa Bastani is an Associate Professor of Operations, Information, and Decisions at the Wharton School, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications spanning healthcare operations, social good, and revenue management. She has received several recognitions for her work, including the Wagner Prize for Excellence in Practice in 2021, the Pierskalla Award for the best paper in healthcare in 2016, 2019, and 2021, and the Behavioral Operations Management Best Paper Award in 2021. Bastani's work also includes significant contributions to healthcare systems, such as improving access to essential medicines in low- and middle-income countries through decision-aware machine learning frameworks, and optimizing pharmaceutical supply chains using machine learning for demand forecasting. Her research extends to algorithmic fairness in human-AI collaboration, clinical trial design with surrogate outcomes, and learning across high-dimensional bandit problems. She completed her PhD at Stanford University and spent a year as a Herman Goldstine postdoctoral fellow at IBM Research.

Research topics

• Pathology
• Environmental health
• Business
• Medicine

Selected publications

• Efficient and targeted COVID-19 border testing via reinforcement learning

  Nature · 2021 · 97 citations

  1st authorCorresponding

  that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.

  DOI

• Improving Human Sequential Decision Making with Reinforcement Learning

  Management Science · 2025-05-22 · 6 citations

  article1st authorCorresponding

  Workers spend a significant amount of time learning how to make good decisions. Evaluating the efficacy of a given decision, however, can be complicated—for example, decision outcomes are often long-term and relate to the original decision in complex ways. Surprisingly, even though learning good decision-making strategies is difficult, the strategies can often be expressed in simple and concise forms. Focusing on sequential decision making, we design a novel machine learning algorithm that is ca…

  PublisherDOI

• Multitask Learning and Bandits via Robust Statistics

  Management Science · 2025-01-06 · 5 citations

  articleSenior author

  Decision makers often simultaneously face many related but heterogeneous learning problems. For instance, a large retailer may wish to learn product demand at different stores to solve pricing or inventory problems, making it desirable to learn jointly for stores serving similar customers; alternatively, a hospital network may wish to learn patient risk at different providers to allocate personalized interventions, making it desirable to learn jointly for hospitals serving similar patient popula…

  PublisherDOI

• Unmasking Human Trafficking Risk in Commercial Sex Supply Chains with Machine Learning

  Manufacturing & Service Operations Management · 2025-03-18 · 4 citations

  article

  Problem definition: The covert nature of sex trafficking provides a significant barrier to generating large-scale, data-driven insights to inform law enforcement, policy, and social work. Existing research has focused on analyzing commercial sex sales on the internet to capture scalable geographical proxies for trafficking. However, ads selling commercial sex do not reveal information about worker consent. Therefore, it is challenging to identify risk for trafficking, which involves fraud, coerc…

  PublisherDOI

• The Human-AI Contracting Paradox

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Osbert Bastani

  California University of Pennsylvania

  20 shared

• Mohsen Bayati

  14 shared

• Joel Goh

  National University of Singapore

  7 shared

• Kimon Drakopoulos

  University of Southern California

  6 shared

• Kan Xu

  Chinese Academy of Medical Sciences & Peking Union Medical College

  6 shared

• David Simchi‐Levi

  4 shared

• John Silberholz

  Ross School

  4 shared

• Ruihao Zhu

  Cornell University

  4 shared

Labs

• Operations, Information and Decisions DepartmentPI

Awards & honors

• Wagner Prize for Excellence in Practice (2021)
• Pierskalla Award for the best paper in healthcare (2016, 201…
• Behavioral OM Best Paper Award (2021)
• First place in the George Nicholson and MSOM student paper c…

Similar researchers at University of Pennsylvania

• Andrew B. Abelh-60
• Raffi Amith-58
• Eric T Bradlowh-53